{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D   \n",
    "from matplotlib.patches import Circle, Wedge\n",
    "from matplotlib.collections import PatchCollection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第二回：艺术画笔见乾坤"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、概述"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. matplotlib的三层api\n",
    "matplotlib的原理或者说基础逻辑是，用Artist对象在画布(canvas)上绘制(Render)图形。  \n",
    "就和人作画的步骤类似：  \n",
    "1. 准备一块画布或画纸\n",
    "2. 准备好颜料、画笔等制图工具\n",
    "3. 作画\n",
    "    \n",
    "所以matplotlib有三个层次的API：  \n",
    "  \n",
    "`matplotlib.backend_bases.FigureCanvas` 代表了绘图区，所有的图像都是在绘图区完成的  \n",
    "`matplotlib.backend_bases.Renderer` 代表了渲染器，可以近似理解为画笔，控制如何在 FigureCanvas 上画图。  \n",
    "`matplotlib.artist.Artist` 代表了具体的图表组件，即调用了Renderer的接口在Canvas上作图。  \n",
    "前两者处理程序和计算机的底层交互的事项，第三项Artist就是具体的调用接口来做出我们想要的图，比如图形、文本、线条的设定。所以通常来说，我们95%的时间，都是用来和matplotlib.artist.Artist类打交道的。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T07:51:58.496608Z",
     "start_time": "2021-05-23T07:51:58.471601Z"
    }
   },
   "source": [
    "### 2. Artist的分类\n",
    "Artist有两种类型：`primitives` 和`containers`。 \n",
    "  \n",
    "`primitive`是基本要素，它包含一些我们要在绘图区作图用到的标准图形对象，如**曲线Line2D，文字text，矩形Rectangle，图像image**等。   \n",
    "  \n",
    "`container`是容器，即用来装基本要素的地方，包括**图形figure、坐标系Axes和坐标轴Axis**。他们之间的关系如下图所示：  \n",
    "![分类](https://img-blog.csdnimg.cn/20201122230916134.jpeg?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYwNDk2MQ==,size_16,color_FFFFFF,t_70#pic_center)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可视化中常见的artist类可以参考下图这张表格，解释下每一列的含义。  \n",
    "第一列表示matplotlib中子图上的辅助方法，可以理解为可视化中不同种类的图表类型，如柱状图，折线图，直方图等，这些图表都可以用这些辅助方法直接画出来，属于更高层级的抽象。 \n",
    "\n",
    "第二列表示不同图表背后的artist类，比如折线图方法`plot`在底层用到的就是`Line2D`这一artist类。\n",
    "\n",
    "第三列是第二列的列表容器，例如所有在子图中创建的`Line2D`对象都会被自动收集到`ax.lines`返回的列表中。 \n",
    "\n",
    "下一节的具体案例更清楚地阐释了这三者的关系，其实在很多时候，我们只用记住第一列的辅助方法进行绘图即可，而无需关注具体底层使用了哪些类，但是了解底层类有助于我们绘制一些复杂的图表，因此也很有必要了解。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Axes helper method   | Artist | Container   |\n",
    "| -------------------  | ------ | ----------- |\n",
    "| `bar` - bar charts | `Rectangle` | ax.patches  |\n",
    "| `errorbar` - error bar plots | `Line2D` and `Rectangle` | ax.lines and ax.patches |\n",
    "| `fill` - shared area | `Polygon` | ax.patches   |\n",
    "| `hist` - histograms | `Rectangle` | ax.patches    |\n",
    "|`imshow` - image data | `AxesImage` | ax.images     |\n",
    "| `plot` - xy plots | `Line2D` | ax.lines        |\n",
    "| `scatter` - scatter charts | `PolyCollection` | ax.collections   |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、基本元素 - primitives\n",
    "各容器中可能会包含多种`基本要素-primitives`, 所以先介绍下primitives，再介绍容器。\n",
    "  \n",
    "本章重点介绍下 `primitives` 的几种类型：**曲线-Line2D，矩形-Rectangle，多边形-Polygon，图像-image** \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 2DLines\n",
    "在matplotlib中曲线的绘制，主要是通过类 `matplotlib.lines.Line2D` 来完成的。   \n",
    "  \n",
    "matplotlib中`线-line`的含义：它表示的可以是连接所有顶点的实线样式，也可以是每个顶点的标记。此外，这条线也会受到绘画风格的影响，比如，我们可以创建虚线种类的线。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "它的构造函数：\n",
    "\n",
    ">```\n",
    "class matplotlib.lines.Line2D(xdata, ydata, linewidth=None, linestyle=None, color=None, marker=None, markersize=None, markeredgewidth=None, markeredgecolor=None, markerfacecolor=None, markerfacecoloralt='none', fillstyle=None, antialiased=None, dash_capstyle=None, solid_capstyle=None, dash_joinstyle=None, solid_joinstyle=None, pickradius=5, drawstyle=None, markevery=None, **kwargs)\n",
    ">```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其中常用的的参数有：  \n",
    "+ **xdata**:需要绘制的line中点的在x轴上的取值，若忽略，则默认为range(1,len(ydata)+1)\n",
    "+ **ydata**:需要绘制的line中点的在y轴上的取值\n",
    "+ **linewidth**:线条的宽度\n",
    "+ **linestyle**:线型\n",
    "+ **color**:线条的颜色\n",
    "+ **marker**:点的标记，详细可参考[markers API](https://matplotlib.org/api/markers_api.html#module-matplotlib.markers)\n",
    "+ **markersize**:标记的size\n",
    "  \n",
    "其他详细参数可参考[Line2D官方文档](https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a. 如何设置Line2D的属性\n",
    "有三种方法可以用设置线的属性。  \n",
    "1) 直接在plot()函数中设置  \n",
    "2) 通过获得线对象，对线对象进行设置   \n",
    "3) 获得线属性，使用setp()函数设置  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:16.033193Z",
     "start_time": "2021-05-23T08:29:15.893804Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1) 直接在plot()函数中设置\n",
    "x = range(0,5)\n",
    "y = [2,5,7,8,10]\n",
    "plt.plot(x,y, linewidth=10); # 设置线的粗细参数为10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:16.160728Z",
     "start_time": "2021-05-23T08:29:16.036194Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2) 通过获得线对象，对线对象进行设置\n",
    "x = range(0,5)\n",
    "y = [2,5,7,8,10]\n",
    "line, = plt.plot(x, y, '-') # 这里等号坐标的line,是一个列表解包的操作，目的是获取plt.plot返回列表中的Line2D对象\n",
    "line.set_antialiased(False); # 关闭抗锯齿功能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:16.287757Z",
     "start_time": "2021-05-23T08:29:16.162728Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3) 获得线属性，使用setp()函数设置\n",
    "x = range(0,5)\n",
    "y = [2,5,7,8,10]\n",
    "lines = plt.plot(x, y)\n",
    "plt.setp(lines, color='r', linewidth=10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b. 如何绘制lines\n",
    "1) 绘制直线line  \n",
    "2) errorbar绘制误差折线图  \n",
    "  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "介绍两种绘制直线line常用的方法:    \n",
    "+ **plot方法绘制**  \n",
    "+ **Line2D对象绘制**  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:16.415785Z",
     "start_time": "2021-05-23T08:29:16.288756Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<matplotlib.lines.Line2D object at 0x000002355D5858B0>, <matplotlib.lines.Line2D object at 0x000002355D585C40>]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. plot方法绘制\n",
    "x = range(0,5)\n",
    "y1 = [2,5,7,8,10]\n",
    "y2= [3,6,8,9,11]\n",
    "\n",
    "fig,ax= plt.subplots()\n",
    "ax.plot(x,y1)\n",
    "ax.plot(x,y2)\n",
    "print(ax.lines); # 通过直接使用辅助方法画线，打印ax.lines后可以看到在matplotlib在底层创建了两个Line2D对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:16.527324Z",
     "start_time": "2021-05-23T08:29:16.416784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Line2D对象绘制\n",
    "\n",
    "x = range(0,5)\n",
    "y1 = [2,5,7,8,10]\n",
    "y2= [3,6,8,9,11]\n",
    "fig,ax= plt.subplots()\n",
    "lines = [Line2D(x, y1), Line2D(x, y2,color='orange')]  # 显式创建Line2D对象\n",
    "for line in lines:\n",
    "    ax.add_line(line) # 使用add_line方法将创建的Line2D添加到子图中\n",
    "ax.set_xlim(0,4)\n",
    "ax.set_ylim(2, 11);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2) errorbar绘制误差折线图**  \n",
    "pyplot里有个专门绘制误差线的功能，通过`errorbar`类实现，它的构造函数： \n",
    "  \n",
    ">matplotlib.pyplot.errorbar(x, y, yerr=None, xerr=None, fmt='', ecolor=None, elinewidth=None, capsize=None, barsabove=False, lolims=False, uplims=False, xlolims=False, xuplims=False, errorevery=1, capthick=None, \\*, data=None, \\**kwargs)\n",
    "  \n",
    "其中最主要的参数是前几个:  \n",
    "+ **x**：需要绘制的line中点的在x轴上的取值  \n",
    "+ **y**：需要绘制的line中点的在y轴上的取值  \n",
    "+ **yerr**：指定y轴水平的误差  \n",
    "+ **xerr**：指定x轴水平的误差   \n",
    "+ **fmt**：指定折线图中某个点的颜色，形状，线条风格，例如‘co--’  \n",
    "+ **ecolor**：指定error bar的颜色  \n",
    "+ **elinewidth**：指定error bar的线条宽度  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制errorbar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:16.623411Z",
     "start_time": "2021-05-23T08:29:16.529325Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "x = np.arange(10)\n",
    "y = 2.5 * np.sin(x / 20 * np.pi)\n",
    "yerr = np.linspace(0.05, 0.2, 10)\n",
    "plt.errorbar(x, y + 3, yerr=yerr, label='both limits (default)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. patches\n",
    "matplotlib.patches.Patch类是二维图形类，并且它是众多二维图形的父类，它的所有子类见[matplotlib.patches API](https://matplotlib.org/stable/api/patches_api.html)  ，  \n",
    "Patch类的构造函数：  \n",
    "\n",
    ">Patch(edgecolor=None, facecolor=None, color=None,\n",
    "  linewidth=None, linestyle=None, antialiased=None,\n",
    "  hatch=None, fill=True, capstyle=None, joinstyle=None,\n",
    "  **kwargs)  \n",
    "\n",
    "本小节重点讲述三种最常见的子类，矩形，多边形和楔型。\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a. Rectangle-矩形\n",
    "`Rectangle`矩形类在官网中的定义是： 通过锚点xy及其宽度和高度生成。\n",
    "Rectangle本身的主要比较简单，即xy控制锚点，width和height分别控制宽和高。它的构造函数：\n",
    "\n",
    "> class matplotlib.patches.Rectangle(xy, width, height, angle=0.0, **kwargs)\n",
    "\n",
    "在实际中最常见的矩形图是**`hist直方图`和`bar条形图`**。  \n",
    "  \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1) hist-直方图**  \n",
    "\n",
    ">matplotlib.pyplot.hist(x,bins=None,range=None, density=None, bottom=None, histtype='bar', align='mid', log=False, color=None, label=None, stacked=False, normed=None)\n",
    "    \n",
    "下面是一些常用的参数：  \n",
    "+ **x**: 数据集，最终的直方图将对数据集进行统计\n",
    "+ **bins**: 统计的区间分布\n",
    "+ **range**: tuple, 显示的区间，range在没有给出bins时生效\n",
    "+ **density**: bool，默认为false，显示的是频数统计结果，为True则显示频率统计结果，这里需要注意，频率统计结果=区间数目/(总数*区间宽度)，和normed效果一致，官方推荐使用density\n",
    "+ **histtype**: 可选{'bar', 'barstacked', 'step', 'stepfilled'}之一，默认为bar，推荐使用默认配置，step使用的是梯状，stepfilled则会对梯状内部进行填充，效果与bar类似\n",
    "+ **align**: 可选{'left', 'mid', 'right'}之一，默认为'mid'，控制柱状图的水平分布，left或者right，会有部分空白区域，推荐使用默认\n",
    "+ **log**: bool，默认False,即y坐标轴是否选择指数刻度\n",
    "+ **stacked**: bool，默认为False，是否为堆积状图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "hist绘制直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:16.766398Z",
     "start_time": "2021-05-23T08:29:16.625404Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.random.randint(0,100,100) #生成[0-100)之间的100个数据,即 数据集 \n",
    "bins=np.arange(0,101,10) #设置连续的边界值，即直方图的分布区间[0,10),[10,20)... \n",
    "plt.hist(x,bins,color='fuchsia',alpha=0.5)#alpha设置透明度，0为完全透明 \n",
    "plt.xlabel('scores') \n",
    "plt.ylabel('count') \n",
    "plt.xlim(0,100); #设置x轴分布范围 plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Rectangle`矩形类绘制直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:17.129308Z",
     "start_time": "2021-05-23T08:29:16.768398Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z9JLUOINekhpn0EtS4wx6SWqcQS9Jjfs/plhBHtekrhcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(columns = ['data'])\n",
    "df.loc[:,'data'] = x\n",
    "df['fenzu'] = pd.cut(df['data'], bins=bins, right = False,include_lowest=True)\n",
    "\n",
    "df_cnt = df['fenzu'].value_counts().reset_index()\n",
    "df_cnt.loc[:,'mini'] = df_cnt['index'].astype(str).map(lambda x:re.findall('\\[(.*)\\,',x)[0]).astype(int)\n",
    "df_cnt.loc[:,'maxi'] = df_cnt['index'].astype(str).map(lambda x:re.findall('\\,(.*)\\)',x)[0]).astype(int)\n",
    "df_cnt.loc[:,'width'] = df_cnt['maxi']- df_cnt['mini']\n",
    "df_cnt.sort_values('mini',ascending = True,inplace = True)\n",
    "df_cnt.reset_index(inplace = True,drop = True)\n",
    "\n",
    "#用Rectangle把hist绘制出来\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "for i in df_cnt.index:\n",
    "    rect =  plt.Rectangle((df_cnt.loc[i,'mini'],0),df_cnt.loc[i,'width'],df_cnt.loc[i,'fenzu'])\n",
    "    ax1.add_patch(rect)\n",
    "\n",
    "ax1.set_xlim(0, 100)\n",
    "ax1.set_ylim(0, 16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2) bar-柱状图**   \n",
    "  \n",
    ">matplotlib.pyplot.bar(left, height, alpha=1, width=0.8, color=, edgecolor=, label=, lw=3)\n",
    "  \n",
    "下面是一些常用的参数：    \n",
    "+ **left**：x轴的位置序列，一般采用range函数产生一个序列，但是有时候可以是字符串  \n",
    "+ **height**：y轴的数值序列，也就是柱形图的高度，一般就是我们需要展示的数据；  \n",
    "+ **alpha**：透明度，值越小越透明  \n",
    "+ **width**：为柱形图的宽度，一般这是为0.8即可；  \n",
    "+ **color或facecolor**：柱形图填充的颜色；  \n",
    "+ **edgecolor**：图形边缘颜色   \n",
    "+ **label**：解释每个图像代表的含义，这个参数是为legend()函数做铺垫的，表示该次bar的标签      \n",
    "  \n",
    "   \n",
    "有两种方式绘制柱状图\n",
    "+ bar绘制柱状图  \n",
    "+ `Rectangle`矩形类绘制柱状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:17.257348Z",
     "start_time": "2021-05-23T08:29:17.131294Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar绘制柱状图\n",
    "y = range(1,17)\n",
    "plt.bar(np.arange(16), y, alpha=0.5, width=0.5, color='yellow', edgecolor='red', label='The First Bar', lw=3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:17.385378Z",
     "start_time": "2021-05-23T08:29:17.258358Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XVNXW0e/TPHD26Gd3PfkqcB5AklOBoxljJss1L/fRmyrPTlNwAPjSGNMUDOFc4BIWRsJ3jf69behQG9zlwI1Jfgj8PvB3w8Z5rtEri5uAfcA9LPxOrItL0pPsBm4HtiWZT3IZcB3wliQPsPCq47ohM8KyOT8NHAvsGf0ufXbQkCybc11ZJuNO4BWj0yO/CFw6zishpx+QpAb5hqokNchyl6QGWe6S1CDLXZIaZLlLUoMsd0lqkOUuSQ36H+GCu0abe7v5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Rectangle矩形类绘制柱状图\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "for i in range(1,17):\n",
    "    rect =  plt.Rectangle((i+0.25,0),0.5,i)\n",
    "    ax1.add_patch(rect)\n",
    "ax1.set_xlim(0, 16)\n",
    "ax1.set_ylim(0, 16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b. Polygon-多边形\n",
    "matplotlib.patches.Polygon类是多边形类。它的构造函数：\n",
    "  \n",
    ">class matplotlib.patches.Polygon(xy, closed=True, **kwargs)  \n",
    "  \n",
    "xy是一个N×2的numpy array，为多边形的顶点。  \n",
    "closed为True则指定多边形将起点和终点重合从而显式关闭多边形。  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib.patches.Polygon类中常用的是fill类，它是基于xy绘制一个填充的多边形，它的定义：\n",
    "\n",
    ">matplotlib.pyplot.fill(*args, data=None, **kwargs)\n",
    "\n",
    "参数说明 : 关于x、y和color的序列，其中color是可选的参数，每个多边形都是由其节点的x和y位置列表定义的，后面可以选择一个颜色说明符。您可以通过提供多个x、y、[颜色]组来绘制多个多边形。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:17.511490Z",
     "start_time": "2021-05-23T08:29:17.387390Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 用fill来绘制图形\n",
    "x = np.linspace(0, 5 * np.pi, 1000) \n",
    "y1 = np.sin(x)\n",
    "y2 = np.sin(2 * x) \n",
    "plt.fill(x, y1, color = \"g\", alpha = 0.3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c. Wedge-契形\n",
    "matplotlib.patches.Polygon类是多边形类。其基类是matplotlib.patches.Patch，它的构造函数：\n",
    "\n",
    ">class matplotlib.patches.Wedge(center, r, theta1, theta2, width=None, **kwargs)  \n",
    "  \n",
    "一个Wedge-契形 是以坐标x,y为中心，半径为r，从θ1扫到θ2(单位是度)。  \n",
    "如果宽度给定，则从内半径r -宽度到外半径r画出部分楔形。wedge中比较常见的是绘制饼状图。  \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib.pyplot.pie语法：  \n",
    ">matplotlib.pyplot.pie(x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=0, radius=1, counterclock=True, wedgeprops=None, textprops=None, center=0, 0, frame=False, rotatelabels=False, *, normalize=None, data=None)\n",
    "  \n",
    "制作数据x的饼图，每个楔子的面积用x/sum(x)表示。    \n",
    "其中最主要的参数是前4个：  \n",
    "+ **x**：契型的形状，一维数组。\n",
    "+ **explode**：如果不是等于None，则是一个len(x)数组，它指定用于偏移每个楔形块的半径的分数。  \n",
    "+ **labels**：用于指定每个契型块的标记，取值是列表或为None。  \n",
    "+ **colors**：饼图循环使用的颜色序列。如果取值为None，将使用当前活动循环中的颜色。  \n",
    "+ **startangle**：饼状图开始的绘制的角度。   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pie绘制饼状图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:17.607540Z",
     "start_time": "2021-05-23T08:29:17.513509Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n",
    "sizes = [15, 30, 45, 10] \n",
    "explode = (0, 0.1, 0, 0) \n",
    "fig1, ax1 = plt.subplots() \n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90) \n",
    "ax1.axis('equal'); # Equal aspect ratio ensures that pie is drawn as a circle. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "wedge绘制饼图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:17.703570Z",
     "start_time": "2021-05-23T08:29:17.608560Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5))\n",
    "ax1 = fig.add_subplot(111)\n",
    "theta1 = 0\n",
    "sizes = [15, 30, 45, 10] \n",
    "patches = []\n",
    "patches += [\n",
    "    Wedge((0.5, 0.5), .4, 0, 54),           \n",
    "    Wedge((0.5, 0.5), .4, 54, 162),  \n",
    "    Wedge((0.5, 0.5), .4, 162, 324),           \n",
    "    Wedge((0.5, 0.5), .4, 324, 360),  \n",
    "]\n",
    "colors = 100 * np.random.rand(len(patches))\n",
    "p = PatchCollection(patches, alpha=0.8)\n",
    "p.set_array(colors)\n",
    "ax1.add_collection(p);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. collections\n",
    "collections类是用来绘制一组对象的集合，collections有许多不同的子类，如RegularPolyCollection, CircleCollection, Pathcollection, 分别对应不同的集合子类型。其中比较常用的就是散点图，它是属于PathCollection子类，scatter方法提供了该类的封装，根据x与y绘制不同大小或颜色标记的散点图。 它的构造方法：\n",
    "  \n",
    ">Axes.scatter(self, x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=<deprecated parameter>, edgecolors=None, *, plotnonfinite=False, data=None, **kwargs)\n",
    "      \n",
    "      \n",
    "其中最主要的参数是前5个：  \n",
    "+ **x**：数据点x轴的位置  \n",
    "+ **y**：数据点y轴的位置  \n",
    "+ **s**：尺寸大小  \n",
    "+ **c**：可以是单个颜色格式的字符串，也可以是一系列颜色  \n",
    "+ **marker**: 标记的类型  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:17.799637Z",
     "start_time": "2021-05-23T08:29:17.705561Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 用scatter绘制散点图\n",
    "x = [0,2,4,6,8,10] \n",
    "y = [10]*len(x) \n",
    "s = [20*2**n for n in range(len(x))] \n",
    "plt.scatter(x,y,s=s) ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. images\n",
    "images是matplotlib中绘制image图像的类，其中最常用的imshow可以根据数组绘制成图像，它的构造函数：\n",
    ">class matplotlib.image.AxesImage(ax, cmap=None, norm=None, interpolation=None, origin=None, extent=None, filternorm=True, filterrad=4.0, resample=False, **kwargs)\n",
    "  \n",
    "imshow根据数组绘制图像\n",
    ">matplotlib.pyplot.imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None, vmin=None, vmax=None, origin=None, extent=None, shape=<deprecated parameter>, filternorm=1, filterrad=4.0, imlim=<deprecated parameter>, resample=None, url=None, *, data=None, **kwargs）\n",
    "\n",
    "使用imshow画图时首先需要传入一个数组，数组对应的是空间内的像素位置和像素点的值，interpolation参数可以设置不同的差值方法，具体效果如下。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:18.406235Z",
     "start_time": "2021-05-23T08:29:17.801624Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "methods = [None, 'none', 'nearest', 'bilinear', 'bicubic', 'spline16',\n",
    "           'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',\n",
    "           'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos']\n",
    "\n",
    "\n",
    "grid = np.random.rand(4, 4)\n",
    "\n",
    "fig, axs = plt.subplots(nrows=3, ncols=6, figsize=(9, 6),\n",
    "                        subplot_kw={'xticks': [], 'yticks': []})\n",
    "\n",
    "for ax, interp_method in zip(axs.flat, methods):\n",
    "    ax.imshow(grid, interpolation=interp_method, cmap='viridis')\n",
    "    ax.set_title(str(interp_method))\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、对象容器 - Object container\n",
    "容器会包含一些`primitives`，并且容器还有它自身的属性。  \n",
    "比如`Axes Artist`，它是一种容器，它包含了很多`primitives`，比如`Line2D`，`Text`；同时，它也有自身的属性，比如`xscal`，用来控制X轴是`linear`还是`log`的。  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Figure容器\n",
    "`matplotlib.figure.Figure`是`Artist`最顶层的`container`-对象容器，它包含了图表中的所有元素。一张图表的背景就是在`Figure.patch`的一个矩形`Rectangle`。  \n",
    "当我们向图表添加`Figure.add_subplot()`或者`Figure.add_axes()`元素时，这些都会被添加到`Figure.axes`列表中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:18.548550Z",
     "start_time": "2021-05-23T08:29:18.408222Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.536818;0.775x0.343182)\n",
      "[<AxesSubplot:>, <matplotlib.axes._axes.Axes object at 0x0000020FD0ED21F0>]\n"
     ]
    },
    {
     "data": {
      "image/png": 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0PZIcrarBpOtYb54RLklqZmhIkpoZGpKkZoaGJKmZoSFJamZoSJKaGRqSpGaGhiSpmaEhSWpmaEiSmhkakqRmhoYkqZmhIUlq1hQaSXYmOZFkIcmDy7TfkeStJMe6x0OtYyVJ02PV+2kk2QQ8DNzF8C5+R5IcqqpXlnT9dlX90SWOlSRNgZY9jR3AQlWdrKp3gIPAnsbXX8tYSdJlpiU0tgCvjiwvduuW+u0kLyR5MslvXuRYSdIUWPXwFJBl1i293d9/Ar9cVT9Jshv4JrC9cezwL0nmgDmA2dnZhrIkSePWsqexCNw0srwVODXaoap+XFU/6Z4/AVyd5PqWsSOvsb+qBlU1mJmZuYgpSJLGpSU0jgDbk2xLshnYCxwa7ZDkl5Kke76je93XW8ZKkqbHqoenqupMkgeAp4FNwIGqOp7k/q59H3A38CdJzgD/A+ytqgKWHbtOc5EkrbMM39svL4PBoObn5yddhiQ1S3K0qgaTrmO9eUa4JKmZoSFJamZoSJKaGRqSpGaGhiSpmaEhSWpmaEiSmhkakqRmhoYkqZmhIUlqZmhIkpoZGpKkZk2hkWRnkhNJFpI8uEz7p5O82D2eS3LrSNsPkryU5FgSr0IoSVNs1UujJ9kEPAzcxfCmSkeSHKqqV0a6fR/4eFW9mWQXsB/4yEj7nVV1use6JUkT0LKnsQNYqKqTVfUOcBDYM9qhqp6rqje7xecZ3qFPkrTBtITGFuDVkeXFbt1KPgs8ObJcwDNJjnb3AZckTalVD08BWWbdsnduSnInw9D42Mjqj1bVqSQ3AM8m+W5VHV5m7BwwBzA7O9tQliRp3Fr2NBaBm0aWtwKnlnZK8iHgEWBPVb1+dn1Vner+fA14nOHhrvNU1f6qGlTVYGZmpn0GkqSxaQmNI8D2JNuSbAb2AodGOySZBR4DPlNV3xtZf02Sa88+Bz4BvNxX8ZKk8Vr18FRVnUnyAPA0sAk4UFXHk9zfte8DHgLeD3w5CcCZ7l65NwKPd+uuAr5eVU+ty0wkSesuVct+PTFRg8Gg5uc9pUPS9EhytPuwvKF5RrgkqZmhIUlqZmhIkpoZGpKkZoaGJKmZoSFJamZoSJKaGRqSpGaGhiSpmaEhSWpmaEiSmhkakqRmhoYkqVlTaCTZmeREkoUkDy7TniRf6tpfTHJb61hJ0vRYNTSSbAIeBnYBtwD3JLllSbddwPbuMQd85SLGSpKmRMuexg5goapOVtU7wEFgz5I+e4Cv1dDzwHVJPtA4VpI0JVpCYwvw6sjyYreupU/LWEnSlFj1dq9Allm39HZ/K/VpGTt8gWSO4aEtgP9NciXdS/x64PSkixizK23Oznfj+/VJFzAOLaGxCNw0srwVONXYZ3PDWACqaj+wHyDJ/JVw28SzrrT5wpU3Z+e78SW5Iu5R3XJ46giwPcm2JJuBvcChJX0OAfd2v0V1O/BWVf2ocawkaUqsuqdRVWeSPAA8DWwCDlTV8ST3d+37gCeA3cAC8FPgvguNXZeZSJLWXcvhKarqCYbBMLpu38jzAj7XOrbB/ovsP+2utPnClTdn57vxXRFzzvD9XpKk1XkZEUlSs4mFxlouTTKtGub86W6uLyZ5Lsmtk6izL62XkEnyW0neTXL3OOtbDy1zTnJHkmNJjif5l3HX2KeGn+n3JvnHJC90871vEnX2JcmBJK+tdErARnzfOk9Vjf3B8Evx/wJ+heGv5b4A3LKkz27gSYbnetwO/Pskah3znH8HeF/3fNc0z7llviP9/onh9153T7ruMfwbXwe8Asx2yzdMuu51nu9fAX/bPZ8B3gA2T7r2Ncz594DbgJdXaN9Q71vLPSa1p7GWS5NMq1XnXFXPVdWb3eLzDM9rmVatl5D5M+AbwGvjLG6dtMz5j4HHquqHAFU1zfNumW8B1yYJ8AsMQ+PMeMvsT1UdZjiHlWy0963zTCo01nJpkml1sfP5LMNPLNNq1fkm2QJ8CtjHxtDyb/xrwPuS/HOSo0nuHVt1/WuZ798Dv8HwpN6XgD+vqp+Np7yJ2GjvW+dp+pXbdbCWS5NMq4u5pMqdDEPjY+ta0fpqme/fAZ+vqneHH0SnXsucrwI+DPwB8PPAvyV5vqq+t97FrYOW+f4hcAz4feBXgWeTfLuqfrzOtU3KRnvfOs+kQmMtlyaZVk3zSfIh4BFgV1W9Pqba1kPLfAfAwS4wrgd2JzlTVd8cS4X9a/25Pl1VbwNvJzkM3ApMY2i0zPc+4G9qeMB/Icn3gZuB/xhPiWO30d63zjOpw1NruTTJtFp1zklmgceAz0zpJ89Rq863qrZV1Qer6oPAPwB/OsWBAW0/198CfjfJVUneA3wE+M6Y6+xLy3x/yHCviiQ3Mryo38mxVjleG+196zwT2dOoNVyaZFo1zvkh4P3Al7tP32dqSi/61jjfDaVlzlX1nSRPAS8CPwMeqaqpvKJz47/xXwNfTfISw0M3n6+qqb36bZJHgTuA65MsAl8AroaN+b61HM8IlyQ184xwSVIzQ0OS1MzQkCQ1MzQkSc0MDUlSM0NDktTM0JAkNTM0JEnN/g+1guc60dNu/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(211) # 作一幅2*1的图，选择第1个子图\n",
    "ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.3]) # 位置参数，四个数分别代表了(left,bottom,width,height)\n",
    "print(ax1) \n",
    "print(fig.axes) # fig.axes 中包含了subplot和axes两个实例, 刚刚添加的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于`Figure`维持了`current axes`，因此你不应该手动的从`Figure.axes`列表中添加删除元素，而是要通过`Figure.add_subplot()`、`Figure.add_axes()`来添加元素，通过`Figure.delaxes()`来删除元素。但是你可以迭代或者访问`Figure.axes`中的`Axes`，然后修改这个`Axes`的属性。   \n",
    "  \n",
    "比如下面的遍历axes里的内容，并且添加网格线："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:18.644260Z",
     "start_time": "2021-05-23T08:29:18.550540Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(211)\n",
    "\n",
    "for ax in fig.axes:\n",
    "    ax.grid(True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Figure`也有它自己的`text、line、patch、image`。你可以直接通过`add primitive`语句直接添加。但是注意`Figure`默认的坐标系是以像素为单位，你可能需要转换成figure坐标系：(0,0)表示左下点，(1,1)表示右上点。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure容器的常见属性：**  \n",
    "`Figure.patch`属性：Figure的背景矩形  \n",
    "`Figure.axes`属性：一个Axes实例的列表（包括Subplot)  \n",
    "`Figure.images`属性：一个FigureImages patch列表  \n",
    "`Figure.lines`属性：一个Line2D实例的列表（很少使用）  \n",
    "`Figure.legends`属性：一个Figure Legend实例列表（不同于Axes.legends)  \n",
    "`Figure.texts`属性：一个Figure Text实例列表  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Axes容器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`matplotlib.axes.Axes`是matplotlib的核心。大量的用于绘图的`Artist`存放在它内部，并且它有许多辅助方法来创建和添加`Artist`给它自己，而且它也有许多赋值方法来访问和修改这些`Artist`。  \n",
    "  \n",
    "和`Figure`容器类似，`Axes`包含了一个patch属性，对于笛卡尔坐标系而言，它是一个`Rectangle`；对于极坐标而言，它是一个`Circle`。这个patch属性决定了绘图区域的形状、背景和边框。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:18.740285Z",
     "start_time": "2021-05-23T08:29:18.646261Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAANN0lEQVR4nO3cf4jk9X3H8efLO21IY0zpNZD7kWjpWXKYgvYwlkK1aMvpH3d/pAQPJDWIJ2kNpQkBS0pymL/S0BQC15otFZtANCZ/hIVcuEJqEEJO7oKNeCeG7cXqXQQTY/zniObad/+Ysbvd7Dlfd2d3z30/H3Aw35nPzrz5sPfc2e/sTKoKSdLGd9F6DyBJWhsGX5KaMPiS1ITBl6QmDL4kNWHwJamJicFPcn+SF5I8eZ7bk+QLSeaSPJHkmumPKUlaqSHP8B8A9rzO7TcDO8f/DgD/tPKxJEnTNjH4VfUo8LPXWbIP+FKNHAXekeRd0xpQkjQdm6dwH9uA5xYcnx5f9/zihUkOMPotAC7m99kyhUeXpE6e56dV9VvL+dJpBH+wqpoBZgCyNcVda/nokrQBHOS/lvul0/grnTPAjgXH28fXSZIuINMI/izwofFf61wHvFxVv3I6R5K0viae0knyIHADsCXJaeDTwMUAVXUfcBi4BZgDzgIfXq1hJUnLNzH4VbV/wu0F/OXUJpIkrQrfaStJTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITg4KfZE+Sp5PMJblnidvfneSRJI8neSLJLdMfVZK0EhODn2QTcAi4GdgF7E+ya9GyvwUerqqrgVuBf5z2oJKklRnyDP9aYK6qTlXVq8BDwL5Fawp4+/jyZcCPpzeiJGkaNg9Ysw14bsHxaeD9i9YcBP4tyUeBXwduWuqOkhwADgCjHwuSpDUzrRdt9wMPVNV24Bbgy0l+5b6raqaqdlfVbt46pUeWJA0yJPhngB0LjrePr1voDuBhgKr6HvAWYMs0BpQkTceQ4B8Ddia5IskljF6UnV205lngRoAk72UU/J9Mc1BJ0spMDH5VnQPuBo4ATzH6a5wTSe5Nsne87OPAnUl+ADwI3F5VtVpDS5LeuKxXl7M1xV3r8tCS9OZ1kO9X1e7lfKnvtJWkJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNTEo+En2JHk6yVySe86z5oNJTiY5keQr0x1TkrRSmyctSLIJOAT8CXAaOJZktqpOLlizE/gb4A+r6qUk71ytgSVJyzPkGf61wFxVnaqqV4GHgH2L1twJHKqqlwCq6oXpjilJWqkhwd8GPLfg+PT4uoWuBK5M8t0kR5PsWeqOkhxIcjzJcc4ub2BJ0vJMPKXzBu5nJ3ADsB14NMn7qurnCxdV1QwwA5CtqSk9tiRpgCHP8M8AOxYcbx9ft9BpYLaqfllVPwJ+yOgHgCTpAjEk+MeAnUmuSHIJcCswu2jNNxg9uyfJFkaneE5Nb0xJ0kpNDH5VnQPuBo4ATwEPV9WJJPcm2TtedgR4MclJ4BHgE1X14moNLUl641K1PqfSszXFXevy0JL05nWQ71fV7uV8qe+0laQmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqYlBwU+yJ8nTSeaS3PM66z6QpJLsnt6IkqRpmBj8JJuAQ8DNwC5gf5JdS6y7FPgr4LFpDylJWrkhz/CvBeaq6lRVvQo8BOxbYt1ngM8Cv5jifJKkKRkS/G3AcwuOT4+v+z9JrgF2VNU3X++OkhxIcjzJcc6+4VklSSuweaV3kOQi4PPA7ZPWVtUMMAOQramVPrYkabghz/DPADsWHG8fX/eaS4GrgO8keQa4Dpj1hVtJurAMCf4xYGeSK5JcAtwKzL52Y1W9XFVbquryqrocOArsrarjqzKxJGlZJga/qs4BdwNHgKeAh6vqRJJ7k+xd7QElSdMx6Bx+VR0GDi+67lPnWXvDyseSJE2b77SVpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDUxKPhJ9iR5OslcknuWuP1jSU4meSLJt5O8Z/qjSpJWYmLwk2wCDgE3A7uA/Ul2LVr2OLC7qn4P+Drwd9MeVJK0MkOe4V8LzFXVqap6FXgI2LdwQVU9UlVnx4dHge3THVOStFJDgr8NeG7B8enxdedzB/CtpW5IciDJ8STHObvUCknSatk8zTtLchuwG7h+qduragaYAcjW1DQfW5L0+oYE/wywY8Hx9vF1/0+Sm4BPAtdX1SvTGU+SNC1DTukcA3YmuSLJJcCtwOzCBUmuBr4I7K2qF6Y/piRppSYGv6rOAXcDR4CngIer6kSSe5PsHS/7HPA24GtJ/iPJ7HnuTpK0Tgadw6+qw8DhRdd9asHlm6Y8lyRpynynrSQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0MCn6SPUmeTjKX5J4lbv+1JF8d3/5YksunPqkkaUUmBj/JJuAQcDOwC9ifZNeiZXcAL1XV7wD/AHx22oNKklZmyDP8a4G5qjpVVa8CDwH7Fq3ZB/zr+PLXgRuTZHpjSpJWavOANduA5xYcnwbef741VXUuycvAbwI/XbgoyQHgwPjwFQ7y5HKG3oC2sGivGnMv5rkX89yLeb+73C8cEvypqaoZYAYgyfGq2r2Wj3+hci/muRfz3It57sW8JMeX+7VDTumcAXYsON4+vm7JNUk2A5cBLy53KEnS9A0J/jFgZ5IrklwC3ArMLlozC/z5+PKfAf9eVTW9MSVJKzXxlM74nPzdwBFgE3B/VZ1Ici9wvKpmgX8BvpxkDvgZox8Kk8ysYO6Nxr2Y517Mcy/muRfzlr0X8Ym4JPXgO20lqQmDL0lNrHrw/ViGeQP24mNJTiZ5Ism3k7xnPeZcC5P2YsG6DySpJBv2T/KG7EWSD46/N04k+cpaz7hWBvwfeXeSR5I8Pv5/cst6zLnaktyf5IUkS75XKSNfGO/TE0muGXTHVbVq/xi9yPufwG8DlwA/AHYtWvMXwH3jy7cCX13Nmdbr38C9+GPgrePLH+m8F+N1lwKPAkeB3es99zp+X+wEHgd+Y3z8zvWeex33Ygb4yPjyLuCZ9Z57lfbij4BrgCfPc/stwLeAANcBjw2539V+hu/HMsybuBdV9UhVnR0fHmX0noeNaMj3BcBnGH0u0y/Wcrg1NmQv7gQOVdVLAFX1whrPuFaG7EUBbx9fvgz48RrOt2aq6lFGf/F4PvuAL9XIUeAdSd416X5XO/hLfSzDtvOtqapzwGsfy7DRDNmLhe5g9BN8I5q4F+NfUXdU1TfXcrB1MOT74krgyiTfTXI0yZ41m25tDdmLg8BtSU4Dh4GPrs1oF5w32hNgjT9aQcMkuQ3YDVy/3rOshyQXAZ8Hbl/nUS4Umxmd1rmB0W99jyZ5X1X9fD2HWif7gQeq6u+T/AGj9/9cVVX/s96DvRms9jN8P5Zh3pC9IMlNwCeBvVX1yhrNttYm7cWlwFXAd5I8w+gc5ewGfeF2yPfFaWC2qn5ZVT8CfsjoB8BGM2Qv7gAeBqiq7wFvYfTBat0M6sliqx18P5Zh3sS9SHI18EVGsd+o52lhwl5U1ctVtaWqLq+qyxm9nrG3qpb9oVEXsCH/R77B6Nk9SbYwOsVzag1nXCtD9uJZ4EaAJO9lFPyfrOmUF4ZZ4EPjv9a5Dni5qp6f9EWrekqnVu9jGd50Bu7F54C3AV8bv279bFXtXbehV8nAvWhh4F4cAf40yUngv4FPVNWG+y144F58HPjnJH/N6AXc2zfiE8QkDzL6Ib9l/HrFp4GLAarqPkavX9wCzAFngQ8Put8NuFeSpCX4TltJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpif8FLlM+xzgX3RsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "rect = ax.patch  # axes的patch是一个Rectangle实例\n",
    "rect.set_facecolor('green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Axes`有许多方法用于绘图，如`.plot()、.text()、.hist()、.imshow()`等方法用于创建大多数常见的`primitive`(如`Line2D，Rectangle，Text，Image`等等）。在`primitives`中已经涉及，不再赘述。   \n",
    "  \n",
    "Subplot就是一个特殊的Axes，其实例是位于网格中某个区域的Subplot实例。其实你也可以在任意区域创建Axes，通过Figure.add_axes([left,bottom,width,height])来创建一个任意区域的Axes，其中left,bottom,width,height都是[0—1]之间的浮点数，他们代表了相对于Figure的坐标。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你不应该直接通过`Axes.lines`和`Axes.patches`列表来添加图表。因为当创建或添加一个对象到图表中时，`Axes`会做许多自动化的工作:  \n",
    "它会设置Artist中figure和axes的属性，同时默认Axes的转换；  \n",
    "它也会检视Artist中的数据，来更新数据结构，这样数据范围和呈现方式可以根据作图范围自动调整。  \n",
    "  \n",
    "你也可以使用Axes的辅助方法`.add_line()`和`.add_patch()`方法来直接添加。  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另外Axes还包含两个最重要的Artist container：\n",
    "\n",
    "`ax.xaxis`：XAxis对象的实例，用于处理x轴tick以及label的绘制  \n",
    "`ax.yaxis`：YAxis对象的实例，用于处理y轴tick以及label的绘制  \n",
    "会在下面章节详细说明。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Axes容器**的常见属性有：  \n",
    "`artists`:    Artist实例列表  \n",
    "`patch`:     Axes所在的矩形实例  \n",
    "`collections`: Collection实例  \n",
    "`images`:    Axes图像  \n",
    "`legends`:\t  Legend 实例  \n",
    "`lines`:\t  Line2D 实例  \n",
    "`patches`:\t  Patch 实例  \n",
    "`texts`:\t  Text 实例  \n",
    "`xaxis`:\t  matplotlib.axis.XAxis 实例  \n",
    "`yaxis`:\t  matplotlib.axis.YAxis 实例  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Axis容器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`matplotlib.axis.Axis`实例处理`tick line`、`grid line`、`tick label`以及`axis label`的绘制，它包括坐标轴上的刻度线、刻度`label`、坐标网格、坐标轴标题。通常你可以独立的配置y轴的左边刻度以及右边的刻度，也可以独立地配置x轴的上边刻度以及下边的刻度。\n",
    "\n",
    "刻度包括主刻度和次刻度，它们都是Tick刻度对象。  \n",
    "  \n",
    "`Axis`也存储了用于自适应，平移以及缩放的`data_interval`和`view_interval`。它还有Locator实例和Formatter实例用于控制刻度线的位置以及刻度label。\n",
    "\n",
    "每个Axis都有一个`label`属性，也有主刻度列表和次刻度列表。这些`ticks`是`axis.XTick`和`axis.YTick`实例，它们包含着`line primitive`以及`text primitive`用来渲染刻度线以及刻度文本。\n",
    "\n",
    "刻度是动态创建的，只有在需要创建的时候才创建（比如缩放的时候）。Axis也提供了一些辅助方法来获取刻度文本、刻度线位置等等：  \n",
    "常见的如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:18.867939Z",
     "start_time": "2021-05-23T08:29:18.741276Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2355fa73310>]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([-0.5,  0. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, ''),\n",
       " Text(0, 0, ''),\n",
       " Text(0, 0, ''),\n",
       " Text(0, 0, ''),\n",
       " Text(0, 0, ''),\n",
       " Text(0, 0, ''),\n",
       " Text(0, 0, ''),\n",
       " Text(0, 0, ''),\n",
       " Text(0, 0, ''),\n",
       " Text(0, 0, ''),\n",
       " Text(0, 0, '')]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<a list of 22 Line2D ticklines objects>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([0., 4.])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([-0.2,  4.2])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 不用print，直接显示结果\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "x = range(0,5)\n",
    "y = [2,5,7,8,10]\n",
    "plt.plot(x, y, '-')\n",
    "\n",
    "axis = ax.xaxis # axis为X轴对象\n",
    "axis.get_ticklocs()     # 获取刻度线位置\n",
    "axis.get_ticklabels()   # 获取刻度label列表(一个Text实例的列表）。 可以通过minor=True|False关键字参数控制输出minor还是major的tick label。\n",
    "axis.get_ticklines()    # 获取刻度线列表(一个Line2D实例的列表）。 可以通过minor=True|False关键字参数控制输出minor还是major的tick line。\n",
    "axis.get_data_interval()# 获取轴刻度间隔\n",
    "axis.get_view_interval()# 获取轴视角（位置）的间隔"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面的例子展示了如何调整一些轴和刻度的属性(忽略美观度，仅作调整参考)：  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:18.963976Z",
     "start_time": "2021-05-23T08:29:18.868934Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure() # 创建一个新图表\n",
    "rect = fig.patch   # 矩形实例并将其设为黄色\n",
    "rect.set_facecolor('lightgoldenrodyellow')\n",
    "\n",
    "ax1 = fig.add_axes([0.1, 0.3, 0.4, 0.4]) # 创一个axes对象，从(0.1,0.3)的位置开始，宽和高都为0.4，\n",
    "rect = ax1.patch   # ax1的矩形设为灰色\n",
    "rect.set_facecolor('lightslategray')\n",
    "\n",
    "\n",
    "for label in ax1.xaxis.get_ticklabels(): \n",
    "    # 调用x轴刻度标签实例，是一个text实例\n",
    "    label.set_color('red') # 颜色\n",
    "    label.set_rotation(45) # 旋转角度\n",
    "    label.set_fontsize(16) # 字体大小\n",
    "\n",
    "for line in ax1.yaxis.get_ticklines():\n",
    "    # 调用y轴刻度线条实例, 是一个Line2D实例\n",
    "    line.set_color('green')    # 颜色\n",
    "    line.set_markersize(25)    # marker大小\n",
    "    line.set_markeredgewidth(2)# marker粗细"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Tick容器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`matplotlib.axis.Tick`是从`Figure`到`Axes`到`Axis`到`Tick`中最末端的容器对象。  \n",
    "`Tick`包含了`tick`、`grid line`实例以及对应的`label`。 \n",
    "  \n",
    "所有的这些都可以通过`Tick`的属性获取，常见的`tick`属性有     \n",
    "`Tick.tick1line`：Line2D实例  \n",
    "`Tick.tick2line`：Line2D实例  \n",
    "`Tick.gridline`：Line2D实例  \n",
    "`Tick.label1`：Text实例  \n",
    "`Tick.label2`：Text实例  \n",
    "  \n",
    "y轴分为左右两个，因此tick1对应左侧的轴；tick2对应右侧的轴。   \n",
    "x轴分为上下两个，因此tick1对应下侧的轴；tick2对应上侧的轴。  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面的例子展示了，如何将Y轴右边轴设为主轴，并将标签设置为美元符号且为绿色："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-23T08:29:19.075005Z",
     "start_time": "2021-05-23T08:29:18.965966Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(100*np.random.rand(20))\n",
    "\n",
    "# 设置ticker的显示格式\n",
    "formatter = matplotlib.ticker.FormatStrFormatter('$%1.2f')\n",
    "ax.yaxis.set_major_formatter(formatter)\n",
    "\n",
    "# 设置ticker的参数，右侧为主轴，颜色为绿色\n",
    "ax.yaxis.set_tick_params(which='major', labelcolor='green',\n",
    "                         labelleft=False, labelright=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 思考题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- primitives 和 container的区别和联系是什么，分别用于控制可视化图表中的哪些要素"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 使用提供的drug数据集，对第一列yyyy和第二列state分组求和，画出下面折线图。PA加粗标黄，其他为灰色。    \n",
    "图标题和横纵坐标轴标题，以及线的文本暂不做要求。  \n",
    "    \n",
    "![](https://img-blog.csdnimg.cn/20210523162430365.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 分别用一组长方形柱和填充面积的方式模仿画出下图，函数 y = -1 * (x - 2) * (x - 8) +10 在区间[2,9]的积分面积\n",
    "![](https://img-blog.csdnimg.cn/20201126105910781.png)\n",
    "![](https://img-blog.csdnimg.cn/20201126105910780.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参考资料\n",
    "[1. matplotlib设计的基本逻辑](https://zhuanlan.zhihu.com/p/32693665)  \n",
    "[2. AI算法工程师手册](https://www.bookstack.cn/read/huaxiaozhuan-ai/spilt.2.333f5abdbabf383d.md)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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